A Model to Compare International Hospital Bed Numbers, including a Case Study on the Role of Indigenous People on Acute 'Occupied' Bed Demand in Australian States

• Published in: International journal of environmental research and public health Vol. 19; no. 18; p. 11239
• Main Author: Jones, Rodney P
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07-09-2022; MDPI
• Subjects: Adult; Age; Australia; Bed Occupancy; Capital expenditures; Cardiovascular disease; Case studies; Child; Critical Care; Death; Demand; Diabetes; Fatalities; Female; Funding; Health care; Health care policy; Hospitals; Humans; Indigenous Peoples; Mental disorders; Mental health; Mortality; Native peoples; Nurses; Pediatrics; Population; Pregnancy; Australia; England; United Kingdom > UK; Japan; bed availability; hospital bed numbers; capacity planning; hospital bed occupancy; integrated care; new methods; Northern Territory; Australia; hospital bed modelling; indigenous health; international benchmarking
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph191811239

Comparing international or regional hospital bed numbers is not an easy matter, and a pragmatic method has been proposed that plots the number of beds per 1000 deaths versus the log of deaths per 1000 population. This method relies on the fact that 55% of a person's lifetime hospital bed utilization occurs in the last year of life-irrespective of the age at death. This is called the nearness to death effect. The slope and intercept of the logarithmic relationship between the two are highly correlated. This study demonstrates how lines of equivalent bed provision can be constructed based on the value of the intercept. Sweden looks to be the most bed-efficient country due to long-term investment in integrated care. The potential limitations of the method are illustrated using data from English Clinical Commissioning Groups. The main limitation is that maternity, paediatric, and mental health care do not conform to the nearness to death effect, and hence, the method mainly applies to adult acute care, especially medical and critical care bed numbers. It is also suggested that sensible comparison can only be made by comparing levels of occupied beds rather than available beds. Occupied beds measure the expressed bed demand (although often constrained by access to care issues), while available beds measure supply. The issue of bed supply is made complex by the role of hospital size on the average occupancy margin. Smaller hospitals are forced to operate at a lower average occupancy; hence, countries with many smaller hospitals such as Germany and the USA appear to have very high numbers of available beds. The so-called 85% occupancy rule is an "urban myth" and has no fundamental basis whatsoever. The very high number of "hospital" beds in Japan is simply an artefact arising from "nursing home" beds being counted as a "hospital" bed in this country. Finally, the new method is applied to the expressed demand for occupied acute beds in Australian states. Using data specific to acute care, i.e., excluding mental health and maternity, a long-standing deficit of beds was identified in Tasmania, while an unusually high level of occupied beds in the Northern Territory (NT) was revealed. The high level of demand for beds in the NT appears due to an exceptionally large population of indigenous people in this state, who are recognized to have elevated health care needs relative to non-indigenous Australians. In this respect, indigenous Australians use 3.5 times more occupied bed days per 1000 deaths (1509 versus 429 beds per 1000 deaths) and 6 times more occupied bed days per 1000 population (90 versus 15 beds per 1000 population) than their non-indigenous counterparts. The figure of 1509 beds per 1000 deaths (or 4.13 occupied beds per 1000 deaths) for indigenous Australians is indicative of a high level of "acute" nursing care in the last months of life, probably because nursing home care is not readily available due to remoteness. A lack of acute beds in the NT then results in an extremely high average bed occupancy rate with contingent efficiency and delayed access implications.